Geodesic
Tuning parameters in random forests
Erwan Scornet1
1 CMAP, École Polytechnique, Route de Saclay, 91128 Palaiseau
ESAIM. Proceedings, Tome 60 (2017), pp. 144-162 Cet article a éte moissonné depuis la source EDP Sciences

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Breiman's (2001) random forests are a very popular class of learning algorithms often able to produce good predictions even in high-dimensional frameworks, with no need to accurately tune its inner parameters. Unfortunately, there are no theoretical findings to support the default values used for these parameters in Breiman's algorithm. The aim of this paper is therefore to present recent theoretical results providing some insights on the role and the tuning of these parameters.
DOI : 10.1051/proc/201760144

Erwan Scornet 1

1 CMAP, École Polytechnique, Route de Saclay, 91128 Palaiseau 
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Erwan Scornet. Tuning parameters in random forests. ESAIM. Proceedings, Tome 60 (2017), pp. 144-162. doi: 10.1051/proc/201760144

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